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IMU-Aided Ultra-wideband Based Localization for Coal Mine Robots

  • Meng-gang Li
  • Hua ZhuEmail author
  • Shao-ze You
  • Lei Wang
  • Zheng Zhang
  • Chao-quan Tang
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11740)

Abstract

Robotic mining equipment is playing an increasingly important role in coal mine operations. Due to the complexity of underground occlusion environment, the localization methods available are limited, which restricts the development of coal mine robots (CMRs). Ultra Wideband (UWB) is a promising positioning sensor with accurate ranging capacity, but it needs to overcome the transient signal loss caused by multipath effect and metal block in coal mine. Range measurements from UWB can only provide 3 degrees of freedom (DOF) position without orientation, which is not enough to operation for CMRs under space-constrained coal mine. In this paper, a pseudo-GPS positioning system composed by UWB range measurements is proposed to provide the position of CMRs. Additionally, an Error State Kalman Filter (ESKF) is used for fusing measurements from Inertial Measurement Unit (IMU) and UWB positioning system. The complete 6 DOF state estimation is established, and the biases of IMU and the calibration parameters of IMU w.r.t. the UWB mobile node are also estimated online to accommodate the long-term operation in underground harsh environment. Experiments in different motion conditions show that our approach can provide robust and precise 6 DOF state estimation for CMRs.

Keywords

UWB IMU EKF ESKF State estimation 

Notes

Acknowledgment

This work is supported by the National Key Research and Development Program of China (No. 2018YFC0808000) and the Priority Academic Program Development of Jiangsu Higher Education Institutions (PAPD), China.

References

  1. 1.
    Siciliano, B., Khatib, O.: Springer Handbook of Robotics. Springer, Cham (2016).  https://doi.org/10.1007/978-3-319-32552-1CrossRefzbMATHGoogle Scholar
  2. 2.
    Aghili, F., Salerno, A.: Driftless 3-D attitude determination and positioning of mobile robots by integration of IMU with two RTK GPSs. IEEE/ASME Trans. Mechatron. 18(1), 21–31 (2013)CrossRefGoogle Scholar
  3. 3.
    Kushleyev, A., Mellinger, D., Powers, C., Kumar, V.: Towards a swarm of agile micro quadrotors. Auton. Robots 35(4), 287–300 (2013)CrossRefGoogle Scholar
  4. 4.
    Kanellakis, C., Nikolakopoulos, G.: Evaluation of visual localization systems in underground mining. In: 2016 24th Mediterranean Conference on Control and Automation (MED), pp. 539–544. IEEE(2016)Google Scholar
  5. 5.
    Menggang, L., Hua, Z., Shaoze, Y., Lei, W., Chaoquan, T.: Efficient laser-based 3D SLAM for coal mine rescue robots. IEEE Access 7, 14124–14138 (2019).  https://doi.org/10.1109/access.2018.2889304CrossRefGoogle Scholar
  6. 6.
    Einicke, G.A., Ralston, J.C., Hargrave, C.O.: Longwall mining automation an application of minimum-variance smoothing [Applications of Control]. IEEE Control Syst. Mag. 28(6), 28–37 (2008)MathSciNetCrossRefGoogle Scholar
  7. 7.
    Paraszczak, J., Gustafson, A., Schunnesson, H.: Technical and operational aspects of autonomous LHD application in metal mines. Int. J. Surf. Min. Reclam. Environ. 29(5), 391–403 (2015)Google Scholar
  8. 8.
    Liu, Z., Li, C., Wu, D.: A wireless sensor network based personnel positioning scheme in coal mines with blind areas. Sensors 10(11), 9891–9918 (2010)CrossRefGoogle Scholar
  9. 9.
    Longkang, W., Baisheng, N., Ruming, Z.: ZigBee-based positioning system for coal miners. Proc. Eng. 26, 2406–2414 (2011)CrossRefGoogle Scholar
  10. 10.
    Bulin, M. A., Fan, Y.: The design and implementation of WiFi localization GIS for mine. J. Xi’an Univ. Sci. Technol. 3 (2012)Google Scholar
  11. 11.
    Yang, H., Luo, T., Li, W., et al.: A stable SINS/UWB integrated positioning method of shearer based on the multi-model intelligent switching algorithm. IEEE Access 7, 29128–29138 (2019)CrossRefGoogle Scholar
  12. 12.
    Fang, X., Wang, C., Nguyen, T.M., et al.: Model-free approach for sensor network localization with noisy distance measurement. In: 2018 15th International Conference on Control, Automation, Robotics and Vision (ICARCV), pp. 1973–1978. IEEE (2018)Google Scholar
  13. 13.
    Zhou, Y.: An efficient least-squares trilateration algorithm for mobile robot localization. In: IEEE International Conference on Intelligent Robots and Systems, IROS 2009, pp. 3474–3479. IEEE/RSJ (2009)Google Scholar
  14. 14.
    Agarwal, S., Mierle, K.: Ceres solver (2013). https://github.com/ceres-solver/ceres-solver
  15. 15.
    Moore, T., Stouch, D.: A generalized extended Kalman Filter implementation for the robot operating system. In: Menegatti, E., Michael, N., Berns, K., Yamaguchi, H. (eds.) Intelligent Autonomous Systems 13. AISC, vol. 302, pp. 335–348. Springer, Cham (2016).  https://doi.org/10.1007/978-3-319-08338-4_25CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Meng-gang Li
    • 1
    • 2
  • Hua Zhu
    • 1
    • 2
    Email author
  • Shao-ze You
    • 1
    • 2
  • Lei Wang
    • 1
    • 2
  • Zheng Zhang
    • 1
    • 2
  • Chao-quan Tang
    • 1
    • 2
  1. 1.School of Mechanical and Electrical EngineeringChina University of Mining and TechnologyXuzhouChina
  2. 2.Jiangsu Collaborative Innovation Center of Intelligent Mining EquipmentChina University of Mining and TechnologyXuzhouChina

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